Attempting to merge populations

README.md

@@ -20,3 +20,8 @@ const y = ((cx,)->cx^2).(X[:, 2]) + cos.(X[:, 3]) .+ 5.0;
 
 The default number of processes is 10; this is set with
 `addprocs(10)` in `paralleleureqa.jl`.
+
+### Hyperparameters
+
+Turn on annealing by setting the following in `paralleleureqa.jl`
+`const annealing = true`

eureqa.jl

@@ -386,6 +386,12 @@ function bestOfSample(pop::Population)::PopMember
     return sample.members[best_idx]
 end
 
+# Return best 10 examples
+function bestSubPop(pop::Population)::Population
+    best_idx = sortperm([pop.members[member].score for member=1:pop.n])
+    return Population(pop.members[best_idx[1:10]])
+end
+
 # Mutate the best sampled member of the population
 function iterateSample(pop::Population, T::Float64)::PopMember
     allstar = bestOfSample(pop)

paralleleureqa.jl

@@ -1,24 +1,36 @@
 using Distributed
-addprocs(10)
+const nthreads = 10
+addprocs(nthreads)
 @everywhere include("eureqa.jl")
 
 println("Lets try to learn (x2^2 + cos(x3) + 5) using regularized evolution from scratch")
 const npop = 100
-const nthreads = 10
 const annealing = false
+const niterations = 10
 bestScore = Inf
+
+# Generate random initial populations
 allPops = [Population(npop, 3) for i=1:nthreads]
 
-@everywhere f = (pop,)->run(pop, 10000, annealing)
-allPops = pmap(f, allPops)
+# Create a mapping for running the algorithm on all processes
+@everywhere f = (pop,)->run(pop, 1000, annealing)
+
+# Do niterations cycles
+for i=1:niterations
+    # Map it over our workers
+    global allPops = deepcopy(pmap(f, allPops))
 
-for pop in allPops
-    bestCurScoreIdx = argmin([pop.members[member].score for member=1:pop.n])
-    bestCurScore = pop.members[bestCurScoreIdx].score
-    if bestCurScore < bestScore
-        global bestScore = bestCurScore
-        println(bestScore, " is the score for ", stringTree(pop.members[bestCurScoreIdx].tree))
+    # Get best 10 models for each processes
+    bestPops = Population(vcat(map(((pop,)->bestSubPop(pop).members), allPops)...))
+    for pop in bestPops
+        bestCurScoreIdx = argmin([pop.members[member].score for member=1:pop.n])
+        bestCurScore = pop.members[bestCurScoreIdx].score
+        if bestCurScore < bestScore
+            global bestScore = bestCurScore
+            println(bestScore, " is the score for ", stringTree(pop.members[bestCurScoreIdx].tree))
+        end
     end
+    exit()
 end